rm(list = ls())
library(here)
## here() starts at /Users/kurtingeman/github/CORE
library(tidyr)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(ggplot2)
library(fmsb)
library(forcats)
library(RColorBrewer)
load(here("CORE EDM analysis", "Output", "Rdata", "4_IS", "SMAP_best_embeddings_ESU_rec4n.RData"))
rec4_ESU <- out_results
rm(out_results)
load(here("CORE EDM analysis", "Output", "Rdata", "4_IS", "SMAP_best_embeddings_Imnaha_rec4n.RData"))
rec4_IMN <- out_results
rm(out_results)
load(here("CORE EDM analysis", "Output", "Rdata", "4_IS", "SMAP_best_embeddings_Middle Fork Salmon_rec4n.RData"))
rec4_MFS <- out_results
rm(out_results)
load(here("CORE EDM analysis", "Output", "Rdata", "4_IS", "SMAP_best_embeddings_Upper Salmon_rec4n.RData"))
rec4_UPS <- out_results
rm(out_results)
rec4_ESU<- rec4_ESU %>%
mutate(domain = case_when(
grepl("npgo", FP) ~ "ocean",
grepl("pdo", FP) ~ "ocean",
grepl("arc", FP) ~ "ocean",
grepl("upw", FP) ~ "ocean",
grepl("hatch", FP) ~ "human",
grepl("harv", FP) ~ "human",
grepl("hseal", FP) ~ "pred",
grepl("orca", FP) ~ "pred",
grepl("csl", FP) ~ "pred",
grepl("ssl", FP) ~ "pred",
TRUE ~"other"))
rec4_IMN<- rec4_IMN %>%
mutate(domain = case_when(
grepl("npgo", FP) ~ "ocean",
grepl("pdo", FP) ~ "ocean",
grepl("arc", FP) ~ "ocean",
grepl("upw", FP) ~ "ocean",
grepl("hatch", FP) ~ "human",
grepl("harv", FP) ~ "human",
grepl("hseal", FP) ~ "pred",
grepl("orca", FP) ~ "pred",
grepl("csl", FP) ~ "pred",
grepl("ssl", FP) ~ "pred",
TRUE ~"other"))
rec4_MFS <- rec4_MFS %>%
mutate(domain = case_when(
grepl("npgo", FP) ~ "ocean",
grepl("pdo", FP) ~ "ocean",
grepl("arc", FP) ~ "ocean",
grepl("upw", FP) ~ "ocean",
grepl("hatch", FP) ~ "human",
grepl("harv", FP) ~ "human",
grepl("hseal", FP) ~ "pred",
grepl("orca", FP) ~ "pred",
grepl("csl", FP) ~ "pred",
grepl("ssl", FP) ~ "pred",
TRUE ~"other"))
rec4_UPS <- rec4_UPS %>%
mutate(domain = case_when(
grepl("npgo", FP) ~ "ocean",
grepl("pdo", FP) ~ "ocean",
grepl("arc", FP) ~ "ocean",
grepl("upw", FP) ~ "ocean",
grepl("hatch", FP) ~ "human",
grepl("harv", FP) ~ "human",
grepl("hseal", FP) ~ "pred",
grepl("orca", FP) ~ "pred",
grepl("csl", FP) ~ "pred",
grepl("ssl", FP) ~ "pred",
TRUE ~"other"))
unique(rec4_ESU$FP)
## [1] "rec4n" "rec4n_-1" "rec4n_-3" "rec4n_-5" "npgo.win.3"
## [6] "upw.tdmi.5" "rec4n_-4" "rec4n_-2" "pdo.spr.2" "arc.win.2"
unique(rec4_ESU$embedding)
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54
rec4_mods <- rec4_ESU %>%
group_by(embedding, FP) %>%
summarise() %>%
group_by(FP) %>%
mutate(
best_mod = min(embedding), # lowest number (highest rank) model
scale_mod = 1 / best_mod, # above expressed as 0-1
rank_mod = mean(embedding)/54, # average rank of model that that they are in
total_num = length(embedding), # number of models that they are in
prop_mod = total_num/54, # proportion of model that they are in
weight = 54-embedding, # reverse of rank
integrated = sum(weight)/(54*55/2)) %>% # integrate rank and weight
slice(1) %>%
filter(!grepl("rec4", FP)) %>%
arrange(prop_mod) %>%
ungroup()
## `summarise()` has grouped output by 'embedding'. You can override using the `.groups` argument.
# organize vars by Ocean, People, Biol
levels(rec4_mods$FP)
## NULL
rec4_spider <- data.frame(rbind(rep(1,4), rep(0,4),
rec4_mods $rank_mod, rec4_mods $prop_mod, rec4_mods $scale_mod
))
colnames(rec4_spider) <- rec4_mods$FP
trans.pal <- c("#7BCAE44D", "#E47BCA4D", "#CAE47B4D")
pal <- c("#7BCAE4", "#E47BCA", "#CAE47B")
# op <- par(mar = c(1, 1, 1, 1))
# par(mar = c(1, 0, 1, 5))
radarchart(rec4_spider, axistype=0,
#custom polygon
pcol=pal, pfcol=trans.pal, plwd=2, plty=1, seg = 3,
#custom the grid
cglcol="grey", cglty=1, cglwd=0.8,
#custom labels
vlcex=.9, vlabels = c("ARC", "PDO", "NPGO", "UPW"),
title="What variables are found in top models?")
# legend(x=.9, y=.8, legend = c("Ave Rank", "No. Models", "Highest Rank"), bty = "n", pch=20 , col=pal, text.col = "grey", cex=1, pt.cex=2)
# par(op)
rec4_ESU <- rec4_ESU %>%
filter(!grepl("rec4", FP)) %>%
group_by(FP) %>%
mutate(mu = mean(value, na.rm=TRUE)) %>%
ungroup()
ggplot(rec4_ESU, aes(x = FP, y = value)) +
geom_violin()
ggplot(rec4_ESU, aes(x = value, color = FP, fill = FP)) +
geom_density(alpha = 0.4) +
geom_vline(aes(xintercept = mu, color = FP),
linetype = "dashed")
# Tough to see differences in this style
unique(rec4_ESU$FP)
## [1] "npgo.win.3" "upw.tdmi.5" "pdo.spr.2" "arc.win.2"
rec4_ESU$FP = factor(rec4_ESU$FP, levels=c('upw.tdmi.5',
'pdo.spr.2',
'arc.win.2',
'npgo.win.3'))
ggplot(rec4_ESU, aes(x = value, color = FP, fill = FP)) +
geom_histogram(position = "identity", alpha = 0.4) +
scale_color_manual(values = colorRampPalette(brewer.pal(9, "Blues"))(8)[4:7]) +
scale_fill_manual(values = colorRampPalette(brewer.pal(9, "Blues"))(8)[4:7]) +
geom_vline(aes(xintercept = mu, color = FP),
linetype = "dashed")+
facet_grid(FP ~ .)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(rec4_ESU, aes(x = value, color = FP, fill = FP)) +
geom_density(alpha = 0.4) +
scale_color_manual(values = colorRampPalette(brewer.pal(9, "Blues"))(8)[4:7]) +
scale_fill_manual(values = colorRampPalette(brewer.pal(9, "Blues"))(8)[4:7]) +
geom_vline(aes(xintercept = mu, color = FP),
linetype = "dashed")+
facet_grid(FP ~ .)
ggplot(rec4_ESU, aes(x = year, y = value, fill = FP, color = FP)) +
geom_smooth() +
scale_fill_manual(values = colorRampPalette(brewer.pal(9, "Blues"))(8)[4:7]) +
scale_color_manual(values = colorRampPalette(brewer.pal(9, "Blues"))(8)[4:7]) +
geom_hline(aes(yintercept = 0),
linetype = "dashed") +
facet_grid(FP ~ .)
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
rec4_ESU_ts <- rec4_ESU %>%
filter(year > 1957) %>%
filter(year < 2016) %>%
mutate(year = factor(year)) %>%
group_by(year, FP) %>%
summarise(IS = mean(value, na.rm = TRUE),
sd = sd(value, na.rm = TRUE),
n = n()) %>%
mutate(se = sd / sqrt(n),
lower = IS - qt(1 - (0.05 / 2), n - 1) * se,
upper = IS + qt(1 - (0.05 / 2), n - 1) * se) %>%
mutate(year = as.integer(year)) %>%
ungroup() %>% ungroup()
## `summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
ggplot(rec4_ESU_ts, aes(x = year, color = FP, fill = FP)) +
geom_line(aes(y = IS)) +
geom_line(aes(y = upper), alpha = 0.5) +
geom_line(aes(y = lower), alpha = 0.5) +
geom_hline(aes(yintercept = 0),
linetype = "dashed") +
scale_fill_manual(values = colorRampPalette(brewer.pal(9, "Blues"))(8)[4:7]) +
scale_color_manual(values = colorRampPalette(brewer.pal(9, "Blues"))(8)[4:7]) +
facet_grid(FP ~ .)
rec4_ESU_lines <- rec4_ESU %>%
filter(year > 1957) %>%
filter(year < 2016) %>%
mutate(year = factor(year)) %>%
group_by(year, FP, embedding) %>%
mutate(IS = mean(value)) %>%
mutate(year = as.integer(year))
ggplot() +
geom_line(rec4_ESU_lines, mapping = aes(x=year, y=IS, col=FP, group=embedding)) +
geom_hline(rec4_ESU_lines, mapping = aes(yintercept = 0),linetype = "dashed", color = "red") +
scale_fill_manual(values = colorRampPalette(brewer.pal(9, "Blues"))(8)[4:7]) +
scale_color_manual(values = colorRampPalette(brewer.pal(9, "Blues"))(8)[4:7]) +
geom_line(rec4_ESU_ts, mapping = aes(x=year, y=IS), color = "black") +
facet_grid(FP ~ ., scales = "free") +
theme_bw()
unique(rec4_IMN$FP)
## [1] "rec4n" "rec4n_-3" "flow.gageht.4"
## [4] "npgo.yrsum.3" "pdo.spr.4" "upw.tdmi.4"
## [7] "harv.COL.4" "orca.SRKWpodJKL.4" "rec4n_-2"
## [10] "rec4n_-5" "rec4n_-4" "rec4n_-1"
## [13] "csl.males6.5" "hatch.all.1"
unique(rec4_IMN$embedding)# 54
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
## [51] 51 52 53 54
rec4_mods <- rec4_IMN %>%
group_by(embedding, FP) %>%
summarise() %>%
group_by(FP) %>%
mutate(
best_mod = min(embedding), # lowest number (highest rank) model
scale_mod = 1 / best_mod, # above expressed as 0-1
rank_mod = mean(embedding)/54, # average rank of model that that they are in
total_num = length(embedding), # number of models that they are in
prop_mod = total_num/54, # proportion of model that they are in
weight = 54-embedding, # reverse of rank
integrated = sum(weight)/(54*55/2)) %>% # integrate rank and weight
slice(1) %>%
filter(!grepl("rec4", FP)) %>%
filter(!FP == "flow.gageht.4") %>%
ungroup()
## `summarise()` has grouped output by 'embedding'. You can override using the `.groups` argument.
unique(rec4_mods$FP)
## [1] "csl.males6.5" "harv.COL.4" "hatch.all.1"
## [4] "npgo.yrsum.3" "orca.SRKWpodJKL.4" "pdo.spr.4"
## [7] "upw.tdmi.4"
# organize vars by Ocean, People, Biol
trans.pal <- c("#7BCAE44D", "#E47BCA4D", "#CAE47B4D")
pal <- c("#7BCAE4", "#E47BCA", "#CAE47B")
temp <- rec4_mods %>%
select(FP,prop_mod, scale_mod, rank_mod) %>%
mutate(ord = c(1,5,6,2,7,3,4)) %>%
arrange(ord)
rec4_spider <- data.frame(rbind(rep(1,7), rep(0,7),
temp$rank_mod, temp$prop_mod, temp$scale_mod
))
colnames(rec4_spider) <- temp$FP
radarchart(rec4_spider, axistype=0,
#custom polygon
pcol=pal, pfcol=trans.pal, plwd=2, plty=1, seg = 3,
#custom the grid
cglcol="grey", cglty=1, cglwd=0.8,
#custom labels
vlcex=.9, vlabels = c("CSL", "NPGO", "PDO", "UPW", "Harvest",
"Hatchery", "ORCA"),
title="What variables are found in top models?")
rec4_IMN<- rec4_IMN %>%
filter(!grepl("rec4", FP)) %>%
filter(!grepl("flow", FP)) %>%
group_by(FP) %>%
mutate(mu = mean(value, na.rm=TRUE)) %>%
ungroup()
ggplot(rec4_IMN, aes(x = FP, y = value)) +
geom_violin()
rec4_IMN$domain = factor(rec4_IMN$domain, levels = c( "ocean" , "human", "pred"))
unique(rec4_IMN$FP)
## [1] "npgo.yrsum.3" "pdo.spr.4" "upw.tdmi.4"
## [4] "harv.COL.4" "orca.SRKWpodJKL.4" "csl.males6.5"
## [7] "hatch.all.1"
rec4_IMN$FP = factor(rec4_IMN$FP,
levels=c('upw.tdmi.4',
'pdo.spr.4',
'npgo.yrsum.3',
'hatch.all.1',
"harv.COL.4",
"csl.males6.5",
"orca.SRKWpodJKL.4"
))
ggplot(rec4_IMN, aes(x = value, color = domain, fill = domain)) +
geom_density(alpha = 0.4) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal) +
geom_vline(aes(xintercept = mu, color = domain),
linetype = "dashed") +
facet_grid(FP ~ .)
ggplot(rec4_IMN, aes(x = year, y = value, fill = domain, color = domain)) +
geom_smooth() +
scale_fill_manual(values = pal) +
scale_color_manual(values = pal) +
geom_hline(aes(yintercept = 0),
linetype = "dashed") +
facet_grid(FP ~ ., scales = "free")
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
rec4_IMN_ts <- rec4_IMN %>%
filter(year > 1957) %>%
filter(year < 2016) %>%
mutate(year = factor(year)) %>%
group_by(year, FP) %>%
mutate(IS = mean(value, na.rm = TRUE),
sd = sd(value, na.rm = TRUE),
n = n()) %>%
mutate(se = sd / sqrt(n),
lower = IS - qt(1 - (0.05 / 2), n - 1) * se,
upper = IS + qt(1 - (0.05 / 2), n - 1) * se) %>%
mutate(year = as.integer(year))
## Warning in qt(1 - (0.05/2), n - 1): NaNs produced
## Warning in qt(1 - (0.05/2), n - 1): NaNs produced
## Warning in qt(1 - (0.05/2), n - 1): NaNs produced
## Warning in qt(1 - (0.05/2), n - 1): NaNs produced
ggplot(rec4_IMN_ts, aes(x = year, color = domain, fill = domain)) +
geom_line(aes(y = IS)) +
geom_line(aes(y = upper), alpha = 0.5) +
geom_line(aes(y = lower), alpha = 0.5) +
geom_hline(aes(yintercept = 0),
linetype = "dashed") +
scale_fill_manual(values = pal) +
scale_color_manual(values = pal) +
facet_wrap(~ FP + domain, scales = "free")
rec4_IMN_lines <- rec4_IMN %>%
filter(year > 1957) %>%
filter(year < 2016) %>%
mutate(year = factor(year)) %>%
group_by(year, FP, embedding) %>%
mutate(IS = mean(value)) %>%
mutate(year = as.integer(year))
ggplot() +
geom_line(rec4_IMN_lines, mapping = aes(x=year, y=IS, col=domain, group=embedding)) +
scale_fill_manual(values = pal) +
scale_color_manual(values = pal) +
geom_hline(rec4_IMN_lines, mapping = aes(yintercept = 0),linetype = "dashed", color = "red") +
geom_line(rec4_IMN_ts, mapping = aes(x=year, y=IS), color = "black") +
facet_wrap(FP + domain ~ ., scales = "free") +
theme_bw()
unique(rec4_MFS$FP)
## [1] "rec4n" "rec4n_-1" "rec4n_-4" "arc.win.5"
## [5] "harv.CRsport.4" "pdo.sum.2" "rec4n_-3" "npgo.spr.3"
## [9] "rec4n_-2" "upw.tdmi.4" "hatch.SNAK.3" "rec4n_-5"
unique(rec4_MFS$embedding)
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
rec4_mods <- rec4_MFS %>%
group_by(embedding, FP) %>%
summarise() %>%
group_by(FP) %>%
mutate(
best_mod = min(embedding), # lowest number (highest rank) model
scale_mod = 1 / best_mod, # above expressed as 0-1
rank_mod = mean(embedding)/44, # average rank of model that that they are in
total_num = length(embedding), # number of models that they are in
prop_mod = total_num/44, # proportion of model that they are in
weight = 44-embedding, # reverse of rank
integrated = sum(weight)/(44*45/2)) %>% # integrate rank and weight
slice(1) %>%
filter(!grepl("rec4", FP)) %>%
arrange(prop_mod) %>%
ungroup()
## `summarise()` has grouped output by 'embedding'. You can override using the `.groups` argument.
unique(rec4_mods$FP)
## [1] "hatch.SNAK.3" "npgo.spr.3" "upw.tdmi.4" "pdo.sum.2"
## [5] "arc.win.5" "harv.CRsport.4"
temp <- rec4_mods %>%
select(FP, prop_mod, scale_mod, rank_mod) %>%
mutate(ord = c(1,2,4,3,5,6)) %>%
arrange(ord)
rec4_spider <- data.frame(rbind(rep(1,6), rep(0,6),
temp$rank_mod, temp$prop_mod, temp$scale_mod
))
colnames(rec4_spider) <- temp$FP
trans.pal <- c("#7BCAE44D", "#E47BCA4D", "#CAE47B4D")
pal <- c("#7BCAE4", "#E47BCA", "#CAE47B")
# op <- par(mar = c(1, 1, 1, 1))
# par(mar = c(1, 0, 1, 5))
radarchart(rec4_spider, axistype=0,
#custom polygon
pcol=pal, pfcol=trans.pal, plwd=2, plty=1, seg = 3,
#custom the grid
cglcol="grey", cglty=1, cglwd=0.8,
#custom labels
vlcex=.9, vlabels = c("Hatchery", "NPGO", "PDO", "UPW", "ARC", "Harvest"),
title="What variables are found in top models?")
# legend(x=.9, y=.8, legend = c("Ave Rank", "No. Models", "Highest Rank"), bty = "n", pch=20 , col=pal, text.col = "grey", cex=1, pt.cex=2)
# par(op)
rec4_MFS<- rec4_MFS %>%
filter(!grepl("rec4", FP)) %>%
filter(!grepl("flow", FP)) %>%
group_by(FP) %>%
mutate(mu = mean(value, na.rm=TRUE)) %>%
ungroup()
ggplot(rec4_MFS, aes(x = FP, y = value)) +
geom_violin()
rec4_MFS$domain = factor(rec4_MFS$domain, levels = c( "ocean", "pred", "human"))
unique(rec4_MFS$FP)
## [1] "arc.win.5" "harv.CRsport.4" "pdo.sum.2" "npgo.spr.3"
## [5] "upw.tdmi.4" "hatch.SNAK.3"
rec4_MFS$FP = factor(rec4_MFS$FP,
levels=c('arc.win.5',
'upw.tdmi.4',
'pdo.sum.2',
'npgo.spr.3',
'hatch.SNAK.3',
"harv.CRsport.4"
))
ggplot(rec4_MFS, aes(x = year, y = value, fill = domain, color = domain)) +
geom_smooth() +
scale_fill_manual(values = pal) +
scale_color_manual(values = pal) +
geom_hline(aes(yintercept = 0),
linetype = "dashed") +
facet_grid(FP ~ ., scales = "free")
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
# distribution
ggplot(rec4_MFS, aes(x = value, color = domain, fill = domain)) +
geom_density(alpha = 0.4) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal) +
geom_vline(aes(xintercept = mu, color = domain),
linetype = "dashed") +
facet_grid(FP ~ .)
# 3. How do predator interaction strengths (and vars from other domains) vary through time? ### Times series of partial Derivatives, averaged across stocks, for each var
rec4_MFS_ts <- rec4_MFS %>%
filter(year > 1957) %>%
filter(year < 2016) %>%
mutate(year = factor(year)) %>%
group_by(year, FP) %>%
mutate(IS = mean(value, na.rm = TRUE),
sd = sd(value, na.rm = TRUE),
n = n()) %>%
mutate(se = sd / sqrt(n),
lower = IS - qt(1 - (0.05 / 2), n - 1) * se,
upper = IS + qt(1 - (0.05 / 2), n - 1) * se) %>%
mutate(year = as.integer(year))
ggplot(rec4_MFS_ts, aes(x = year, color = domain, fill = domain)) +
geom_line(aes(y = IS)) +
geom_line(aes(y = upper), alpha = 0.5) +
geom_line(aes(y = lower), alpha = 0.5) +
geom_hline(aes(yintercept = 0),
linetype = "dashed") +
scale_fill_manual(values = pal) +
scale_color_manual(values = pal) +
facet_wrap(~ FP + domain, scales = "free")
rec4_MFS_lines <- rec4_MFS %>%
filter(year > 1957) %>%
filter(year < 2016) %>%
mutate(year = factor(year)) %>%
group_by(year, FP, embedding) %>%
mutate(IS = mean(value)) %>%
mutate(year = as.integer(year))
ggplot() +
geom_line(rec4_MFS_lines, mapping = aes(x=year, y=IS, col=domain, group=embedding)) +
scale_fill_manual(values = pal) +
scale_color_manual(values = pal) +
geom_hline(rec4_MFS_lines, mapping = aes(yintercept = 0),linetype = "dashed", color = "red") +
geom_line(rec4_MFS_ts, mapping = aes(x=year, y=IS), color = "black") +
facet_wrap(FP + domain ~ ., scales = "free") +
theme_bw()
unique(rec4_UPS$FP)
## [1] "rec4n" "rec4n_-1" "harv.PACtot.5"
## [4] "hseal.COL.3" "hatch.total.1" "pdo.sum.5"
## [7] "ssl.COL.4" "orca.SRKWdeathsJKL.5" "rec4n_-3"
## [10] "csl.Dpups.0" "npgo.spr.2" "upw.tdmi.5"
## [13] "rec4n_-2" "flow.mean.1"
rec4_mods <- rec4_UPS %>%
group_by(embedding, FP) %>%
summarise() %>%
group_by(FP) %>%
mutate(
best_mod = min(embedding), # lowest number (highest rank) model
scale_mod = 1 / best_mod, # above expressed as 0-1
rank_mod = mean(embedding)/16, # average rank of model that that they are in
total_num = length(embedding), # number of models that they are in
prop_mod = total_num/12, # proportion of model that they are in
weight = 17-embedding, # reverse of rank
integrated = sum(weight)/(16*17/2)) %>% # integrate rank and weight
slice(1) %>%
filter(!grepl("rec4", FP)) %>%
arrange(prop_mod) %>%
filter(!FP == "flow.mean.1") %>%
filter(!FP == "hseal.COL.3") %>%
ungroup()
## `summarise()` has grouped output by 'embedding'. You can override using the `.groups` argument.
rec4_mods$FP <- factor(rec4_mods$FP, levels = c("npgo.spr.2",
"pdo.sum.5",
"upw.tdmi.5",
"harv.PACtot.5",
"hatch.total.1",
"csl.Dpups.0",
"orca.SRKWdeathsJKL.5",
"ssl.COL.4"))
# organize vars by Ocean, People, Biol
levels(rec4_mods$FP)
## [1] "npgo.spr.2" "pdo.sum.5" "upw.tdmi.5"
## [4] "harv.PACtot.5" "hatch.total.1" "csl.Dpups.0"
## [7] "orca.SRKWdeathsJKL.5" "ssl.COL.4"
temp <- rec4_mods %>%
select(FP, prop_mod, scale_mod, rank_mod) %>%
mutate(ord = c(2,1,7,3,4,5,6,8)) %>%
arrange(ord)
rec4_spider <- data.frame(rbind(rep(1,8), rep(0,8),
temp$rank_mod, temp$prop_mod, temp$scale_mod
))
colnames(rec4_spider) <- temp$FP
trans.pal <- c("#7BCAE44D", "#E47BCA4D", "#CAE47B4D")
pal <- c("#7BCAE4", "#E47BCA", "#CAE47B")
# op <- par(mar = c(1, 1, 1, 1))
# par(mar = c(1, 0, 1, 5))
radarchart(rec4_spider, axistype=0,
#custom polygon
pcol=pal, pfcol=trans.pal, plwd=2, plty=1, seg = 3,
#custom the grid
cglcol="grey", cglty=1, cglwd=0.8,
#custom labels
vlcex=.9, vlabels = c("CSL", "NPGO", "PDO", "UPW", "Harvest",
"Hatchery", "ORCA", "SSL"),
title="What variables are found in top models?")
# legend(x=.9, y=.8, legend = c("Ave Rank", "No. Models", "Highest Rank"), bty = "n", pch=20 , col=pal, text.col = "grey", cex=1, pt.cex=2)
# par(op)
rec4_UPS<- rec4_UPS %>%
filter(!grepl("rec4", FP)) %>%
filter(!grepl("flow", FP)) %>%
group_by(FP) %>%
mutate(mu = mean(value, na.rm=TRUE)) %>%
ungroup()
ggplot(rec4_UPS, aes(x = FP, y = value)) +
geom_violin()
rec4_UPS$domain = factor(rec4_UPS$domain, levels = c( "ocean", "pred", "human"))
rec4_UPS$FP = factor(rec4_UPS$FP,
levels=c('upw.tdmi.5',
'pdo.sum.5',
'npgo.spr.2',
'hatch.total.1',
"harv.PACtot.5",
"hseal.COL.3",
"csl.Dpups.0",
"ssl.COL.4",
"orca.SRKWdeathsJKL.5"
))
ggplot(rec4_UPS, aes(x = value, color = domain, fill = domain)) +
geom_density(alpha = 0.4) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal) +
geom_vline(aes(xintercept = mu, color = domain),
linetype = "dashed") +
facet_grid(FP ~ .)
ggplot(rec4_UPS, aes(x = year, y = value, fill = domain, color = domain)) +
geom_smooth() +
scale_fill_manual(values = pal) +
scale_color_manual(values = pal) +
geom_hline(aes(yintercept = 0),
linetype = "dashed") +
facet_grid(FP ~ ., scales = "free")
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
rec4_UPS_ts <- rec4_UPS %>%
filter(year > 1957) %>%
filter(year < 2016) %>%
mutate(year = factor(year)) %>%
group_by(year, FP) %>%
mutate(IS = mean(value, na.rm = TRUE),
sd = sd(value, na.rm = TRUE),
n = n()) %>%
mutate(se = sd / sqrt(n),
lower = IS - qt(1 - (0.05 / 2), n - 1) * se,
upper = IS + qt(1 - (0.05 / 2), n - 1) * se) %>%
mutate(year = as.integer(year))
ggplot(rec4_UPS_ts, aes(x = year, color = domain, fill = domain)) +
geom_line(aes(y = IS)) +
geom_line(aes(y = upper), alpha = 0.5) +
geom_line(aes(y = lower), alpha = 0.5) +
geom_hline(aes(yintercept = 0),
linetype = "dashed") +
scale_fill_manual(values = pal) +
scale_color_manual(values = pal) +
facet_wrap(~ FP + domain, scales = "free")
rec4_UPS_lines <- rec4_UPS %>%
filter(year > 1957) %>%
filter(year < 2016) %>%
mutate(year = factor(year)) %>%
group_by(year, FP, embedding) %>%
mutate(IS = mean(value)) %>%
mutate(year = as.integer(year))
ggplot() +
geom_line(rec4_UPS_lines, mapping = aes(x=year, y=IS, col=domain, group=embedding)) +
scale_fill_manual(values = pal) +
scale_color_manual(values = pal) +
geom_hline(rec4_UPS_lines, mapping = aes(yintercept = 0),linetype = "dashed", color = "red") +
geom_line(rec4_UPS_ts, mapping = aes(x=year, y=IS), color = "black") +
facet_wrap(FP + domain ~ ., scales = "free") +
theme_bw()